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Narayan SM, Wan EY, Andrade JG, Avari Silva JN, Bhatia NK, Deneke T, Deshmukh AJ, Chon KH, Erickson L, Ghanbari H, Noseworthy PA, Pathak RK, Roelle L, Seiler A, Singh JP, Srivatsa UN, Trela A, Tsiperfal A, Varma N, Yousuf OK. Visions for digital integrated cardiovascular care: HRS Digital Health Committee perspectives. CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2024; 5:37-49. [PMID: 38765620 PMCID: PMC11096652 DOI: 10.1016/j.cvdhj.2024.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/22/2024] Open
Affiliation(s)
| | - Elaine Y Wan
- Division of Cardiology, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York
| | | | | | | | | | | | - Ki H Chon
- University of Connecticut, Storrs, Connecticut
| | | | | | | | | | - Lisa Roelle
- Washington University School of Medicine, Saint Louis, Missouri
| | | | - Jagmeet P Singh
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | | | - Anthony Trela
- Lucile Packard Children's Hospital, Palo Alto, California
| | - Angela Tsiperfal
- Stanford Arrhythmia Service, Stanford Healthcare, Palo Alto, California
| | | | - Omair K Yousuf
- Inova Heart and Vascular Institute; Carient Heart and Vascular; and University of Virginia Health, Fairfax, Virginia
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Guan Z, Li H, Liu R, Cai C, Liu Y, Li J, Wang X, Huang S, Wu L, Liu D, Yu S, Wang Z, Shu J, Hou X, Yang X, Jia W, Sheng B. Artificial intelligence in diabetes management: Advancements, opportunities, and challenges. Cell Rep Med 2023; 4:101213. [PMID: 37788667 PMCID: PMC10591058 DOI: 10.1016/j.xcrm.2023.101213] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 08/07/2023] [Accepted: 09/08/2023] [Indexed: 10/05/2023]
Abstract
The increasing prevalence of diabetes, high avoidable morbidity and mortality due to diabetes and diabetic complications, and related substantial economic burden make diabetes a significant health challenge worldwide. A shortage of diabetes specialists, uneven distribution of medical resources, low adherence to medications, and improper self-management contribute to poor glycemic control in patients with diabetes. Recent advancements in digital health technologies, especially artificial intelligence (AI), provide a significant opportunity to achieve better efficiency in diabetes care, which may diminish the increase in diabetes-related health-care expenditures. Here, we review the recent progress in the application of AI in the management of diabetes and then discuss the opportunities and challenges of AI application in clinical practice. Furthermore, we explore the possibility of combining and expanding upon existing digital health technologies to develop an AI-assisted digital health-care ecosystem that includes the prevention and management of diabetes.
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Affiliation(s)
- Zhouyu Guan
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Huating Li
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Ruhan Liu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Furong Laboratory, Changsha, Hunan 41000, China
| | - Chun Cai
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Yuexing Liu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Jiajia Li
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xiangning Wang
- Department of Ophthalmology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, China
| | - Shan Huang
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Liang Wu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Dan Liu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Shujie Yu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Zheyuan Wang
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Jia Shu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xuhong Hou
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Xiaokang Yang
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Weiping Jia
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China.
| | - Bin Sheng
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
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Tanenbaum ML, Zaharieva DP, Addala A, Prahalad P, Hooper JA, Leverenz B, Cortes AL, Arrizon-Ruiz N, Pang E, Bishop F, Maahs DM. 'Much more convenient, just as effective': Experiences of starting continuous glucose monitoring remotely following Type 1 diabetes diagnosis. Diabet Med 2022; 39:e14923. [PMID: 35899591 PMCID: PMC9579993 DOI: 10.1111/dme.14923] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 07/20/2022] [Indexed: 11/29/2022]
Abstract
AIM Initiating continuous glucose monitoring (CGM) shortly after Type 1 diabetes diagnosis has glycaemic and quality of life benefits for youth with Type 1 diabetes and their families. The SARS-CoV-2 pandemic led to a rapid shift to virtual delivery of CGM initiation visits. We aimed to understand parents' experiences receiving virtual care to initiate CGM within 30 days of diagnosis. METHODS We held focus groups and interviews using a semi-structured interview guide with parents of youth who initiated CGM over telehealth within 30 days of diagnosis during the SARS-CoV-2 pandemic. Questions aimed to explore experiences of starting CGM virtually. Groups and interviews were audio-recorded, transcribed and analysed using thematic analysis. RESULTS Participants were 16 English-speaking parents (age 43 ± 6 years; 63% female) of 15 youth (age 9 ± 4 years; 47% female; 47% non-Hispanic White, 20% Hispanic, 13% Asian, 7% Black, 13% other). They described multiple benefits of the virtual visit including convenient access to high-quality care; integrating Type 1 diabetes care into daily life; and being in the comfort of home. A minority experienced challenges with virtual care delivery; most preferred the virtual format. Participants expressed that clinics should offer a choice of virtual or in-person to families initiating CGM in the future. CONCLUSION Most parents appreciated receiving CGM initiation education via telehealth and felt it should be an option offered to all families. Further efforts can continue to enhance CGM initiation teaching virtually to address identified barriers.
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Affiliation(s)
- Molly L Tanenbaum
- Division of Endocrinology, Gerontology, and Metabolism, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
- Division of Endocrinology and Diabetes, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
- Stanford Diabetes Research Center, Stanford, California, USA
| | - Dessi P Zaharieva
- Division of Endocrinology and Diabetes, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
| | - Ananta Addala
- Division of Endocrinology and Diabetes, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
| | - Priya Prahalad
- Division of Endocrinology and Diabetes, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
| | - Julie A Hooper
- Division of Endocrinology and Diabetes, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
| | - Brianna Leverenz
- Division of Endocrinology and Diabetes, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
| | - Ana L Cortes
- Division of Endocrinology and Diabetes, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
| | - Nora Arrizon-Ruiz
- Division of Endocrinology and Diabetes, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
| | - Erica Pang
- Division of Endocrinology and Diabetes, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
| | - Franziska Bishop
- Division of Endocrinology and Diabetes, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
| | - David M Maahs
- Division of Endocrinology and Diabetes, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
- Stanford Diabetes Research Center, Stanford, California, USA
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